BioModels Database

January 2011, model of the month by Christine Seeliger
Original model: BIOMD0000000215

The immunesystem is an important part of the body since, it guards the organism against infections and cancer. For this purpose, highly specialized cell types, e.g. T cells and
B cells have evolved as part of the immunesystem.
T cells play an important role during a variety of
immune system responses
to pathogens and cancer. They are able to kill infected and abnormal cells or aid in activating different other cell types as part of
an immune response. One of these different subtypes of T cells are
T helper cells. They mature from naive T cells
during an immune response after antigen stimulation.

Different
lineages of T helper cells secrete different sets of characteristic cytokines
helping to orchestrate the immune response and aiding in activating other cell types of innate and adaptive immunity.
The development of T helper cells into different lineages, depends on the cytokine environment that induces different master regulators
(cf. figure 1).

The work presented in this paper [2], combines experimental work with mathematical modelling to further understand the regulatory
network that underlies T-bet expression.
The authors found experimental evidence that T-bet is expressed in two waves. The first wave is IFN-γ dependent.
The second one relies on IL-12 and is independent of IFN-γ. The second wave also coincides with STAT4 binding to
the T-bet enhancer.
Moreover, the two waves seem to be coordinated by the TCR signal. During the first wave, the
IFN-γ signal acts synergistically with the TCR signal whereas, the expression of the IL-12Rβ2 chain
is repressed. The IL-12 mediated phase begins with the end of
the TCR signal, thereby releasing the IL-12Rβ2 repression that renders the cells responsive to the IL-12 signal.

Figure 1:
The different T helper cell lineages, their master regulators and characteristic cytokines. Figure taken from [1].

Figure 2:
Previous Model (One Loop Model) of T-bet regulation based on the current literature. Figure taken from [2].

To properly explain the regulations underlying the experimental observations, the authors analyse two mathematical models. The first model, termed One-Loop Model, is based on the current literature knowledge (cf. figure 2). The attempt to fit this model simultaneously to the experimental T-bet, IL-12Rβ2 and IFN-γ expression profiles fails.

In contrast, the Two-Loop model proposed by the authors (cf. figure 3) explains the regulatory mechanisms that lead to the observed expression profiles. The Two-Loop model contains two new features, IL-12 dependent T-bet expression and antigen-dependent IL-12Rβ2 repression. The authors show, that a model including both additional regulatory circuits performs better
than models that only include one of them.

The Two-Loop Model was validated by comparing experimental results with the simulation results for these experiments.
Blocking either IL-12 or IFN-γ signaling gave equivalent results in simulations and
experiments as well (cf. figure 4).
The absence of
IFN-γ, results in the complete loss of the first T-bet wave in the model as well as experiments.
IL-12 absence strongly reduced the second T-bet peak. The upregulation of the IL-12Rβ2 chain (cf. figure 4A/B)
during the second IL-12 dependent T-bet wave, supports the existence of the T-bet IL-12β2 feedback. Furthermore, IFN-γ
seems to accelerate this feedback, which can be seen from the delaying effect in the IFN-γ deficient experiments/simulations
(cf. figure 4, blue curves). Both loops act independently of each other and sequentially in time.

The paper provides further experimental evidence, that the proposed Two-Loop Model contains the
necessary core regulations for primary Th1 activation. Repression of Stat4 and Gata3 transcription do not seem to
play a major role under the used conditions. In addition, the second wave of T-bet expression seems to imprint Th1 cells for
later IFN-γ production in recall responses. The frequency of IFN-γ producing cells
shows T-bet dosage dependence pointing towards a certain time window where T-bet expression is decisive.

The presented study is a good example for the integration of experimental studies with mathematical modelling.
The authors
use experimental data to propose a mathematical model explaining their observations. They are able to show that, their new
model performs much better than the previous explanations available in the current literature. The mathematical model helps
to understand the core regulatory features that underlie the two waves of T-bet expression during T-cell priming and thus
underlines their regulatory hypothesis. It also allowed to predict effects that occured if certain parts of the
included regulatory pathways were knocked down. This study clearly shows the benefit of modelling for hypothesis testing
in experimental studies.